Alina Carare and Ashoka Mody June

 Results: 1.Even prior to the extreme volatility recently experienced, output growth volatility was flattening or mildly rising in some countries 2.More widespread was increased tendency from mid-1990s for shocks to transmit to other countries 3.Higher sensitivity to foreign shocks appears related to vertical specialization

 Extreme volatility should not have come as a complete surprise  “Great Moderation” – was robustly established trend in industrial countries  Domestic volatility declining due to improved policy management and innovations in private sector  But these analyses did not factor in ongoing integration of global economy

 Even when considering multiple countries, these analyses dealt with individual country experiences  Stock and Watson (2005) was the exception  Traced the source of “Great Moderation” to a fall in common international shocks

 Expand Stock and Watson (2005) analysis  From G7 to 22 OECD countries  Using data until 2007Q4  To capture the effects of an increasingly integrated global economy to a perspective on economic volatility  The method decomposes GDP growth volatility into domestic, common international, and spillovers shocks

Y t = vector of stacked detrended growth rates A(L) = matrix lag polynomial First restriction: VAR (p1, p2) Each country growth depends on its own growth (4 lags) and other countries growth (1 lag) Detrending method - Baxter-King (1999) band pass (BP) filter with 8 leads and lags and a pass-band of 6-32 quarters applied to annualized quarter-on-quarter GDP growth rates Volatility is measured as the time-varying variance of this model

 For each date t a regression is estimated by weighted least squares using two-sided exponential weighting  Observation at date s receives a weight of δ |t−s| and δ = 0.97  Observations further away from the point of interest t receive an exponentially-lower weight  s takes values between 1960:Q1 and 2007:Q4, while t takes values between 1977:Q1 and 2006:Q4  Results are robust to different discount factors and length of sample

 VAR errors are decomposed into common international shocks and country-specific shocks:  Where are common international factors or shocks,  Γ is the 22 x k matrix of factor loadings (22 countries times k factors), and  are country-specific or idiosyncratic shocks.  Common international shocks and the domestic shocks are assumed to be uncorrelated and  Second VAR restriction: common shocks affect all countries at once, while country-specific shocks affect other countries after one quarter, spillovers  Parameters estimated using Gaussian maximum likelihood  Variance for each shock is calculated using spectral decomposition

1st result (part II):...but there was a tendency to rise mildly in others

 -variance of 4-quarter ahead forecast errors in a given country in period p  where p=1 or 2 correspond to or  Variance decomposition attributes a portion of to each of the 24 shocks (international shock, domestic shock, and 22 spillover shocks), where, variance in period p attributed to shock j  Change in the variance between two periods is:  where variance component can be rewritten as, where is a term depending on the cumulative impulse response to shock j in period p and is the variance of shock j in period p  Change in contribution of the jth shocks can be decomposed as:  In other words, change in variance can be decomposed into contribution from change in shock variance plus contribution from change in impulse response

 Results: 1.Even prior to the extreme volatility recently experienced, output growth volatility was flattening or mildly rising in some countries 2.More widespread was increased tendency from mid-1990s for shocks to transmit to other countries 3.Higher sensitivity to foreign shocks appear related to vertical specialization  Policy implications I.Increased spillovers call for stronger ex-post coordination mechanism when shocks are large II.Ex-ante prevention consists of sensible national policies